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dc.contributor.authorKozaitis, S.P.
dc.contributor.authorPetrilak, Robert
dc.contributor.authorFoor, Wesley E.
dc.date.accessioned2017-10-06T18:19:03Z
dc.date.available2017-10-06T18:19:03Z
dc.date.issued1992-07-01
dc.identifier.citationKozaitis, S. P., Petrilak, R., & Foor, W. E. (1992). Feature-based correlation filters for distortion invariance. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 1701 264-273.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1843
dc.descriptionAutomation, Fourier optics, Image processing, Light modulators, Neural networks, Optical correlationen_US
dc.description.abstractIn an optical correlator, binary phase-only filters (BPOFs) that recognize objects that vary in a nonrepeatable way are essential for recognizing objects from actual sensors. An approach is required that is as descriptive as a BPOF yet robust to object and background variations of an unknown or nonrepeatable type. We developed a BPOF that was more robust than a synthetic discriminant function (SDF) filter. This was done by creating a filter that retained the invariant features of a training set. By simulation, our feature-based filter offered a range of performance by setting a parameter to different values. As the value of the parameter was changed, correlation peaks within the training set became more consistent and broader. In addition, the feature-based filter was potentially useful for recognizing objects outside the training set. Furthermore, the feature-based filter was more easily calculated and trained than an SDF filter.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleFeature-based correlation filters for distortion invarianceen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.138334


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